May 6, 2021 · In this article, a new framework for categorizing and predicting customer sentiments was proposed. The customer reviews were collected from an international hotel. In the next step, the customer reviews processed, and then entered into various machine learning algorithms.

In this article, the authors analyzed the RFMT dimensions of the customers. Clustering analysis factors are considered from cluster 0 to cluster 10, using the elbow, dendrogram, silhouette, Calinsky–Harabasz, Davies–Bouldin, and Dunn index.

This study uses cutting-edge machine learning techniques, specifically classification algorithms, to assess and categorize customer data into distinct groups.

Customer segmentation is defined as dividing a company's customers on the basis of demographic (age, gender, marital status) and behavioral (types of products ordered, annual income) aspects.

Customer segmentation, a fundamental aspect of modern marketing and business strategy, involves classifying customers into distinct groups based on shared characteristics and behaviors. This project leverages the power of machine learning, specifically the K-means clustering algorithm, to achieve this goal.

Jul 9, 2023 · In this context, companies can use customer reviews on their products to promote their high sales and gain a competitive edge in the market. This paper focuses on classifying the sentiment of...

Jul 9, 2023 · This paper focuses on classifying the sentiment of customer reviews using machine learning approaches. The models are evaluated using various metrics like confusion matrix, AUROC curve, and classification report.

Mar 16, 2023 · In this article, the authors analyzed the RFMT dimensions of the customers. Clustering analysis factors are considered from cluster 0 to cluster 10, using the elbow, dendrogram, silhouette, Calinsky–Harabasz, Davies–Bouldin, and Dunn index.

Three di↵erent representations of customers as a vector of attributes are created and then utilised by the k-means algorithm to divide users into di↵erent clusters. The first representation is using a elementary feature vector and the other two approaches are using feature vectors produced by graph based machine learning models.

Sep 14, 2020 · Classification is the process of predicting the class with your input data. There are mostly two types of classification in Machine Learning Algorithms, one is supervised learning other is...